Asynchronous Physical-Layer Network Coding: Symbol Misalignment Estimation and Its Effect on Decoding

Shao, Yulin; Liew, Soung Chang; Lu, Lu

doi:10.1109/twc.2017.2732348

Cited by 36 publications

(12 citation statements)

References 21 publications

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“…The receiver then estimates different packets using different prior information. The factorizations in (35) can be depicted by a graphical model [5], [26]- [28], as shown in Fig. 4, where we use a Forney-style factor graph [27] to represent the factorization.…”

Section: Map Estimationmentioning

confidence: 99%

Bayesian Over-The-Air Computation

Shao¹,

Gündüz²,

Liew³

2021

Preprint

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Analog over-the-air computation (OAC) is an efficient solution to a class of uplink data aggregation tasks over a multiple-access channel (MAC), wherein the receiver, dubbed the fusion center, aims to reconstruct a function of the data distributed at edge devices rather than the individual data themselves. Existing OAC relies exclusively on the maximum likelihood (ML) estimation at the fusion center to recover the arithmetic sum of the transmitted signals from different devices. ML estimation, however, is much susceptible to noise. In particular, in the misaligned OAC where there are channel misalignments among transmitted signals, ML estimation suffers from severe error propagation and noise enhancement. To address these challenges, this paper puts forth a Bayesian approach for OAC by letting each edge device transmit two pieces of prior information to the fusion center. Three OAC systems are studied: the aligned OAC with perfectly-aligned signals; the synchronous OAC with misaligned channel gains among the received signals; and the asynchronous OAC with both channelgain and time misalignments. Using the prior information, we devise linear minimum mean squared error (LMMSE) estimators and a sum-product maximum a posteriori (SP-MAP) estimator for the three OAC systems. Numerical results verify that, 1) For the aligned and synchronous OAC, our LMMSE estimator significantly outperforms the ML estimator. In the low signalto-noise ratio (SNR) regime, the LMMSE estimator reduces the mean squared error (MSE) by at least 6 dB; in the high SNR regime, the LMMSE estimator lowers the error floor on the MSE by 86.4%; 2) For the asynchronous OAC, our LMMSE and SP-MAP estimators are on an equal footing in terms of the MSE performance, and are significantly better than the ML estimator. On the other hand, in terms of the computational complexity, the SP-MAP estimator is much more efficient than the LMMSE estimator.

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Section: Map Estimationmentioning

confidence: 99%

Bayesian Over-The-Air Computation

Shao¹,

Gündüz²,

Liew³

2021

Preprint

Self Cite

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“…19 The author proposed a denoising scheme based on the null hypothesis of intrinsic mode function and test signals computed with white and colored noise. 23 The author proposed an improved spectrum sensing algorithm for cognitive radio to improve performance in the presence of colored noise. 21 The author derives a low complexity frequency domain channel estimation for faster than Nyquist (FTN) pilot transmission scenario with AWGN and whitening of colored noise.…”

Section: Literature Reviewmentioning

confidence: 99%

“…22 The author proposed an asynchronous physical layer network coding algorithm for relay nodes considering colored noise and improved packet error rate performance. 23 The author proposed an improved spectrum sensing algorithm for cognitive radio to improve performance in the presence of colored noise. 24 Luo confirms the presence of colored noise in wireless signals and suggests a Kalman filter-based noise removal technique for the real-time application like localization.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance improvement of wireless secret key generation with colored noise for IoT

Soni

Upadhyay

Kumar

2019

Int J Communication

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Achieving security in the Internet of things (IoT) networks by generating symmetric keys from the wireless channel parameters like received signal strength (RSS) is a promising approach. Despite the easy acquisition of the RSS signal, RSS-based security is less explored for IoT. In this work, we analyze the performance of RSS-based wireless physical layer key generation with correlated colored noise components and proposed a low complexity filtering approach to improve the performance for the IoT network. We started with providing a survey of various recent researches related to RSS-based key generation and also discussed correlated colored noise components with a few of the recent works considering them. Further, we analyze various colored noise components in the time domain by the Allan variance and Ljung-Box test. Furthermore, we develop a key generation model and proposed a moving window averaging-based filtering followed by Lloyd max quantization to improve the BDR performance, degraded due to the presence of correlated colored noise components. The simulation results show that the proposed preprocessing technique has a considerable improvement in the BDR performance, and the keys generated have sufficient randomness, which is verified by NIST test.

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“…The detector is global-optimal because it detects s n ,θ n , and ϑ jointly, not separately, and applies the MAP criterion over the received-signal-magnitudes over the whole received packet R, not just the received-signal-magnitude over single r n . A Belief Propagation (BP) algorithm can be constructed for the computation of Pr s n ,θ n , ϑ R [13] [14] [15]. The BP algorithm is based on systematic application of Bayes' rule.…”

Section: B Optimal Detectormentioning

confidence: 99%

Noncoherent Detection for Physical-Layer Network Coding

Wang

Liew

2018

IEEE Trans. Wireless Commun.

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This paper investigates noncoherent detection in a two-way relay channel operated with physical layer network coding (PNC), assuming FSK modulation and short-packet transmissions. For noncoherent detection, the detector has access to the magnitude but not the phase of the received signal. For conventional communication in which a receiver receives the signal from a transmitter only, the phase does not affect the magnitude, hence the performance of the noncoherent detector is independent of the phase. PNC, on the other hand, is a multiuser system in which a receiver receives signals from multiple transmitters simultaneously. The relative phase of the signals from different transmitters affects the received signal magnitude through constructive-destructive interference. In particular, for good performance, the noncoherent detector of a multiuser system such as PNC must take into account the influence of the relative phase on the signal magnitude. Building on this observation, this paper delves into the fundamentals of PNC noncoherent detector design. To avoid excessive overhead, we assume a set-up in which the short packets in the PNC system do not have preambles. We show how the relative phase can be deduced directly from the magnitudes of the received data symbols, and that the knowledge of the relative phase thus deduced can in turn be used to enhance performance of noncoherent detection. Our overall detector design consists of two components: 1) a channel gains estimator that estimates channel gains without preambles; 2) a detector that builds on top of the estimated channel gains to jointly estimate relative phase and detect data using a belief propagation algorithm.Numerical results show that our detector performs nearly as well as a "fictitious" optimal detector that has perfect knowledge of the channel gains and relative phase. Although this paper focuses on PNC with FSK modulation, we believe the insight of this paper applies generally to noncoherent detection in other multiuser systems with other modulations. Specifically, our insight is that the relative phase of overlapped signals affects the signal magnitude in multiuser systems, but fortunately the relative phase 2 can be deduced from the magnitudes and this knowledge can be used to improve detection performance. Index TermsPhysical-layer network coding, noncoherent detection, frequency shift keying, short packet.

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Asynchronous Physical-Layer Network Coding: Symbol Misalignment Estimation and Its Effect on Decoding

Cited by 36 publications

References 21 publications

Bayesian Over-The-Air Computation

Bayesian Over-The-Air Computation

Performance improvement of wireless secret key generation with colored noise for IoT

Noncoherent Detection for Physical-Layer Network Coding

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